The invention relates to a method for processing measurement data of a vehicle for determining the start of a search for a parking space.
Parking information in relation to free parking spaces is used, for example, by parking guidance systems and/or navigation instruments for navigating a vehicle searching for a parking space. Modern inner-city systems operate according to a simple principle. If the number of parking spaces and the inflow and outflow of vehicles are known, the availability of free parking spaces can easily be determined therefrom. Vehicles can be navigated to free parking spaces by way of appropriate signposting along the access roads and dynamic updating of the parking space information. Due to these principles, there are restrictions to the extent that the parking areas need to be clearly delimited and the entry and exit of the vehicles must be monitored exactly at all times. To this end, structural measures, such as e.g. barriers or other access control systems, are required.
Due to these restrictions, navigation is only possible to a small number of free parking spaces. With the required structural measures, it is conventionally only possible to integrate multistory car parks or fenced-off parking areas into a parking guidance system. However, the much greater number of parking spaces at the edge of the road or in non-surrounded parking spaces is not considered since the parking situation in public spaces is largely unknown. Only individual communities or traffic management headquarters offer information for specific areas.
For the search for free parking spaces, an identification of parking spaces along respective streets is desirable, particularly in the inner city and in densely populated areas. To this end, DE 10 2009 028 024 A1 has disclosed the practice of using parking space-seeking vehicles, such as e.g. municipal transport vehicles, such as e.g. regularly operating buses or taxis which have at least one sensor for identifying a parking space. Here, the sensor system can be based on optical and/or non-optical sensors.
Furthermore, community-based applications are known, in which the users of vehicles enter information, for example into an app, when they leave a parking space. This information is then provided to other users of the service. A disadvantage of this is that the information about available parking spaces is only as good as the information provided by the users.
There is a problem with the two described alternatives in that the information about the availability of an individual parking space is very short-lived, that is to say a free parking space is generally occupied within a very short period of time in areas with much parking space-seeking traffic, in which parking space information would be helpful.
Furthermore, under the application number 10 2012 201 472.1, the applicant has described a method for providing parking information in relation to free parking spaces, in which a knowledge database with historical data is produced from established information about available, free parking spaces. The historical data comprise statistical data about free parking spaces, in each case for predetermined streets and/or predetermined times or time intervals. A probability distribution in relation to the expected number of free parking spaces for the selected street or streets is established from the historical data and current information which is established at a given time for one or more selected streets by vehicles situated in traffic. The probability distribution represents parking information in relation to free parking spaces in the selected street or streets. The accuracy of the probability distribution depends, inter alia, on the knowledge about a so-called parking rate λp. The parking rate is calculated according to the equation λp(t)=(1−Pn)λ(t), where λ(t) represents a query rate specifying the number of queries for a parking space in time (i.e. per unit time) for a parking segment, i.e. a considered region in which parking processes are desired. Pn specifies the probability of a free parking space.
Therefore, the more accurately the parking rate λp is known, the more accurately the probability for a free parking space can be determined.
It is an object of the present invention to specify, on the basis of this method from the applicant, a method which can establish the start of a search for a parking space in an automated manner in order to improve the precision of determining the parking rate.
The invention may be used for processing measurement data of a vehicle for determining the start of a search for a parking space. The method described below can be performed on-board, i.e. in the vehicle which is searching for the parking space, or off-board, i.e. by a central computer to which the journey data are transmitted. Furthermore, the proposed method offers the option of performing the calculations online, i.e. in real time during the journey, or offline, i.e. subsequently after the journey.
In a first step, there is an acquisition of a number of journey data vectors, wherein each journey data vector contains information about the speed, position data and the time of acquisition of the speed and the position data. The acquisition of the number of journey data vectors takes place at a given time interval (also referred to as sampling rate below) of the order of seconds, e.g. every second or every five or ten seconds. The journey data vectors therefore follow a fixed sequence in time. The position data can be represented by GPS (global positioning system) data. The position data can be established by a GPS module of the vehicle. The speed can be established either by the speed sensor of the vehicle or from the position data and acquisition times of two successive measurements.
In a next step, there is an establishment of a feature vector at each time of acquisition of a journey data vector, wherein the information about the current and, in terms of time, previous journey data vectors are processed, wherein the feature vector comprises as feature components at least one item of speed information and one item of path information. As a result of this, the progress of the journey of the vehicle is taken into account. In this step, the values of the features are recalculated for each newly acquired journey data vector and combined in a feature vector. Therefore, a feature vector is calculated at each (measurement or acquisition) time, wherein use is made of both current and preceding journey data vectors.
In a next step, there is a classification of each feature vector, wherein each feature vector is assigned to one of two traffic categories. The first traffic category denotes the terminating traffic, wherein the driver is not searching for a parking space, while the second traffic category denotes the parking space-seeking traffic, wherein the driver is searching for a parking space. When determining the traffic category, a probability specifying the probability with which the first or the second traffic category is to be assigned to the feature vector is calculated. In this step, the produced feature vectors are considered individually and classified in relation to two traffic classes, namely a terminating traffic represented by the first traffic category and a parking space-seeking traffic represented by the second traffic category. At the end of this step, a probability specifying the probability with which a feature vector belongs to the parking space-seeking traffic and to the terminating traffic is available for each feature vector.
Finally, there is a segmentation of the feature vectors over the time profile of the established traffic categories, wherein there is a subdivision of the journey from the start to the last acquisition of a journey data vector into two segments in accordance with the determined traffic categories of the feature vectors and the transition from one segment into the other segment represents the start of the search for a parking space. The object of the segmentation is to establish on the basis of the analysis of the time profile of the classification of feature vectors that journey data vector which marks the start of searching for a parking space. The result of the segmentation is a subdivision of the journey into two segments, corresponding to the traffic categories, which forms the basis for calculating the desired information in relation to intensity and localization of the parking space-seeking traffic.
If the start of a search for a parking space is known, it can be used to calculate the probability of an available parking space in the surrounding area with greater accuracy. To this end, use can be made of e.g. the method by the applicant, described at the outset, from DE 10 2012 201 472.1. Furthermore, knowledge about the start of a search for a parking space can also be used by city planners to estimate the parking situation in individual streets or neighborhoods.
In order to keep the amount of data to be processed as low as possible, it may be expedient to undertake pre-filtering of the journey data vectors. Thus, journey data vectors can remain unconsidered in the determination of the start of the search for a parking space if the information about the speed of the journey data vector is greater than a first threshold or less than a second threshold. As a result of this, it is possible to neglect e.g. outer-city journeys and standing phases of the vehicle. The first threshold can lie e.g. between 50 km/h and 100 km/h and is, in particular, 80 km/h. By way of example, the second threshold can lie between 2 km/h and 8 km/h and is, in particular, 4 km/h.
In a further embodiment, the journey data vectors are processed within a feature window, which represents a predetermined route, for establishing a respective feature vector, wherein the feature window includes the journey data vectors from the current position or measurement to the first position or measurement which, on the traveled route, lies further back than the predetermined route. The number of journey data vectors in a feature window can therefore vary as a function of sampling rate and speed. By way of example, if the size of the feature window is 1 km, fewer journey data vectors are contained in the feature window in the case of a higher average speed over the last kilometer than in the case of a lower speed, provided a constant sampling rate is assumed.
In a further embodiment, the feature vector comprises one or more of the following feature components as feature components in addition to the speed information and the path information:                Information about circularity of the traveled route. The circularity takes into account a typical behavior pattern in the case of parking space-seeking vehicles, whose journey route often describes a circular selection of the course (e.g. by circling around a block). Here, the reference variable is the distance of the current position from a centroid of the previously acquired path points, which emerge from the position data of the respective journey data vectors.        Information about PCA circularity of the traveled route. Here, so-called PCA (principal component analysis) is used as an aid to determine the circularity of the course. If PCA is applied to the two-dimensional position vectors of a feature window, what is obtained in addition to the two principal components, which describe the mutually orthogonal axes with the highest variance of the individual path points, is a relative value for the portion of the overall variance of the axes.        Information about a change in direction. Parking space-seeking vehicles often make a turning. On the basis of the current and a previous position, it is possible to calculate the journey direction in the form of an angle (0° to 359°, corresponding to the compass directions) for each journey data vector. In order to be able to calculate a meaningful value for the change of direction as the journey progresses, it is possible to form the arithmetic mean over all changes in direction. Preferably, this is carried out with normalized values.        Information about a target inefficiency. This feature calculates the inefficiency of the course in relation to the target of the journey. The target cannot be determined on the basis of the journey data during the journey, and so this feature can only be formed at the end of the journey, once all journey data vectors are known. The position of the last journey data vector is assumed to be the target position, which represents the location of the found parking space. Therefore, this feature component can only be used in a method which is performed offline after the journey is complete.        
In accordance with one embodiment, the speed information can be an arithmetic mean and/or the median of the average speeds of the journey data vectors considered for establishing a respective feature vector.
In accordance with one embodiment, the path information can be a path inefficiency, which specifies how inefficient the traveled route is by way of the ratio between the actually traveled route in view of the shortest route between the positions of two journey data vectors. The inefficiency of the course is a feature which specifies how inefficient the driven route, selected by the driver, is in view of approaching the target of the journey. This takes into account the characteristic of the classifiers (traffic classes), since vehicles which are part of the terminating traffic attempt to approach the sought target along the fastest and most efficient path, while parking space-seeking vehicles have usually already reached the target and circle around it while searching for a parking space.
Here, provision can be made for that path inefficiency which is the maximum for the processed set of journey data vectors to be processed for a feature vector as path information.
In a further embodiment, the feature vectors are normalized for the purposes of classifying each feature vector. Different feature components (abbreviated to features) have different value ranges. So that feature components with numerically higher value regions do not dominate over feature components with numerically smaller value ranges and in order to make the feature values more comparable, the features are normalized. The effect thereof is that both features with a large value range and features with a small value range are imaged onto the same value range.
In order to calculate normalized feature components, use can be made of a z-normalization, known to a person skilled in the art, in which the mean value and the standard deviation are established for each feature component and said feature components are transformed therewith.
Subsequently, it is expedient for the feature components to be reduced by vector projection, in particular by applying a principal component analysis (PCA). The principal component analysis is an unmonitored process for reducing features. It pursues the target of finding those main axes in a feature space on which the feature vectors imaged thereon achieve maximum variance.
The calculation of the probability of the classifier can then be carried out using Bayes' theorem, which is known to a person skilled in the art from e.g. [1] or [2].
In a further embodiment, the start of the search for a parking space is defined by a positive transition from the first traffic category to the second traffic category, wherein the journey data vector which is assigned to the second traffic category represents the start of the search for a parking space. The converse case, a transition from the second traffic category to the first traffic category, is referred to as a negative transition. In the ideal case, a positive transition occurs at most once during a journey. However, reality shows that a number of positive transitions may occur during a journey. The start of the search for a parking space can then be established using the following alternatives:
In a first alternative, the last positive transition in terms of time from the first classifier to the second classifier is selected as start of the search for a parking space as long as the classification result of the subsequent journey data vectors constantly comprises the second classifier. After a negative transition, the journey data vector marking the start of the search for a parking space is discarded such that, from this time on, no acquired start of the search is present anymore.
In a second alternative, the last positive transition in terms of time from the first classifier to the second classifier is selected as start of the search for a parking space as long as the classification result of the subsequent journey data vectors constantly comprises the second traffic category for a predetermined journey route. This segmentation alternative extends the first alternative with a distance criterion. Here, an established journey data vector is not forgotten immediately after a negative transition, but rather is maintained for a certain distance after the negative transition. If a further positive transition is found within said distance, it is ignored and the journey data vector established earlier is maintained. If no positive transition is found, the earlier journey data vector marking the start of the search for a parking space is forgotten at the end of the route after the negative transition.
In a third alternative, the start of the search for a parking space is established on the basis of an integral of the profile of the probability over the traveled route. In this third alternative, it is not only the hard decision as to whether or not a feature vector constitutes parking space-seeking traffic that is used to establish the start of the search, but also the reliability with which the decision was made. If no start of the search is present and a positive transition is acquired with a new journey data vector, the integral of the profile of the so-called a posteriori probability is continuously calculated over the traveled path. If the result of the integral calculation is negative, the previously established journey data vector is discarded.
The invention furthermore creates a computer program product, which can be loaded directly into the internal memory of a digital computer or computer system, e.g. a computer of a vehicle or a central computer, and comprises software code portions by means of which the steps in accordance with one of the preceding claims are executed when the product runs on the computer.
Below, the invention will be explained in more detail on the basis of an exemplary embodiment in the drawing. In detail:
Other objects, advantages and novel features of the present invention will become apparent from the following detailed description of one or more preferred embodiments when considered in conjunction with the accompanying drawings.